abstract

Experiences with Improving the Transparency of AI Models and Services

Authors:

Stephanie Houde,

Jacquelyn Martino,

Aleksandra Mojsilovic,

David Piorkowski,

Kush R. VarshneyAuthors Info & Claims

CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

Pages 1 - 8

https://doi.org/10.1145/3334480.3383051

Published: 25 April 2020 Publication History

Abstract

AI models and services are used in a growing number of high-stakes areas, resulting in a need for increased transparency. Consistent with this, several proposals for higher quality and more consistent documentation of AI data, models, and systems have emerged. Little is known, however, about the needs of those who would produce or consume these new forms of documentation. Through semi-structured developer interviews, and two document-creation exercises, we have assembled a clearer picture of these needs and the various challenges faced in creating accurate and useful AI documentation. Based on the observations from this work, supplemented by feedback received during multiple design explorations and stakeholder conversations, we make recommendations for easing the collection and flexible presentation of AI facts to promote transparency.

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Cited By

Celepija APalmero Aprosio ALepri BKazhamiakin R(2025)AI product cards: a framework for code-bound formal documentation cards in the public administrationData & Policy10.1017/dap.2024.557Online publication date: 8-Jan-2025
https://doi.org/10.1017/dap.2024.55
Wan QHu SZhang YWang PWen BLu Z(2024)"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language ModelsProceedings of the ACM on Human-Computer Interaction10.1145/36373618:CSCW1(1-26)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637361
Kommiya Mothilal RGuha SAhmed S(2024)Towards a Non-Ideal Methodological Framework for Responsible MLProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642501(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642501
Show More Cited By

Index Terms

Experiences with Improving the Transparency of AI Models and Services
1. Computing methodologies
  1. Artificial intelligence
2. Software and its engineering
  1. Software creation and management

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Information & Contributors

Information

Published In

cover image ACM Conferences

CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

April 2020

4474 pages

ISBN:9781450368193

DOI:10.1145/3334480

General Chairs:
Regina Bernhaupt
Eindhoven University of Technology, Netherlands
,
Florian 'Floyd' Mueller
Monash University, Australia
,
David Verweij
Newcastle University, UK
,
Josh Andres
RMIT, Australia
,
Program Chairs:
Joanna McGrenere
University of British Columbia, Canada
,
Andy Cockburn
University of Canterbury, New Zealand
,
Ignacio Avellino
University of Maryland Baltimore County, USA
,
Alix Goguey
Grenoble Alpes University, France
,
Pernille Bjørn
University of Copenhagen, Denmark
,
Shengdong (Shen) Zhao
National University of Singapore, Singapore
,
Briane Paul Samson
Future University Hakodate, Japan & De La Salle University, Philippines
,
Rafal Kocielnik
University of Washington, USA

Copyright © 2020 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 April 2020

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CHI '20

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CHI '20: CHI Conference on Human Factors in Computing Systems

April 25 - 30, 2020

HI, Honolulu, USA

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Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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Citations

Cited By

Celepija APalmero Aprosio ALepri BKazhamiakin R(2025)AI product cards: a framework for code-bound formal documentation cards in the public administrationData & Policy10.1017/dap.2024.557Online publication date: 8-Jan-2025
https://doi.org/10.1017/dap.2024.55
Wan QHu SZhang YWang PWen BLu Z(2024)"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language ModelsProceedings of the ACM on Human-Computer Interaction10.1145/36373618:CSCW1(1-26)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637361
Kommiya Mothilal RGuha SAhmed S(2024)Towards a Non-Ideal Methodological Framework for Responsible MLProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642501(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642501
Qin KGu D(2024)To Share or Hide: Confidential Model Compilation as a Service with Privacy-Preserving Transparency2024 43rd International Symposium on Reliable Distributed Systems (SRDS)10.1109/SRDS64841.2024.00022(126-138)Online publication date: 30-Sep-2024
https://doi.org/10.1109/SRDS64841.2024.00022
Bhandary ADobariya VYenduri GJhaveri RGochhait SBenedetto F(2024)Enhancing Household Energy Consumption Predictions Through Explainable AI FrameworksIEEE Access10.1109/ACCESS.2024.337355212(36764-36777)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3373552
Fischer MLanquillon C(2024)Evaluation of Generative AI-Assisted Software Design and Engineering: A User-Centered ApproachArtificial Intelligence in HCI10.1007/978-3-031-60606-9_3(31-47)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-3-031-60606-9_3
Liao QSubramonyam HWang JWortman Vaughan J(2023)Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User ExperienceProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580652(1-21)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580652
Vaishampayan SFarzanehpour SBrown C(2023)Procedural Justice and Fairness in Automated Resume Parsers for Tech Hiring: Insights from Candidate Perspectives2023 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL-HCC57772.2023.00019(103-108)Online publication date: 3-Oct-2023
https://doi.org/10.1109/VL-HCC57772.2023.00019
Fehr JJaramillo-Gutierrez GOala LGröschel MBierwirth MBalachandran PWerneck-Leite ALippert C(2022)Piloting a Survey-Based Assessment of Transparency and Trustworthiness with Three Medical AI ToolsHealthcare10.3390/healthcare1010192310:10(1923)Online publication date: 30-Sep-2022
https://doi.org/10.3390/healthcare10101923
Khondker AKwong JMalik SErdman LKeefe DFernandez NTasian GWang HEstrada CNelson CLorenzo ARickard M(2022)The state of artificial intelligence in pediatric urologyFrontiers in Urology10.3389/fruro.2022.10246622Online publication date: 13-Oct-2022
https://doi.org/10.3389/fruro.2022.1024662
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